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B. Samanta, K. B. Al-Balushi and S. A. Al-Araimi, “Artificial Neural Networks and Support Vector Machines with Genetic Algorithm for Bearing Fault Detection,” Engineering Applications of Artificial Intelligence, Vol. 16, No. 7-8, 2003, pp. 657-665. doi:10.1016/j.engappai.2003.09.006

has been cited by the following article:

• TITLE: Damage Detection Method Using Support Vector Machine and First Three Natural Frequencies for Shear Structures

  AUTHORS: Hien HoThu, Akira Mita

  KEYWORDS: Support Vector Machine; Damage Detection; Natural Frequency; System Identification; Damage Location Indicator

  JOURNAL NAME: Open Journal of Civil Engineering, Vol.3 No.2, June 4, 2013

  ABSTRACT: A method is proposed for detecting damage to shear structures by using Support Vector Machine (SVM) and only the first three natural frequencies of the translational modes. This method is able to determine the damage location in any story of a shear building with only two vibration sensors; to obtain modal frequencies, one sensor on the ground detects an input and the other on the roof detects the output. Based on the shifts in the first three natural frequencies, damage location indicators are proposed, and used as new feature vectors for SVM. Simulations of five-story, nine-story and twenty-one-story shear structures and experiments on a five-story steel model were used to test the performance of the proposed method.